Once feedback reaches dozens of messages, teams either build what the loudest customer asks for or ask AI to “summarise pain points” and receive a polished page with no path to a decision. The useful unit is evidence: who was trying to do what, what blocked them, how often it appears, what can be verified and who needs to act next.
This workflow turns tickets, interview excerpts, sales notes and reviews into a product-decision ledger. ChatGPT or Claude can speed up extraction and clustering; they should not set product priority or turn one complaint into market demand.
Preserve the original wording
Store source, date, customer type, verbatim text, context, status and a link before creating a demand label. Add structured fields afterwards: feedback type, product area, user task, blocker, stated impact, evidence strength, cluster, owner and next action. “Impact” describes task interruption, not development priority.
Use AI for factual extraction
Require structured output: feedback_type, user_task, product_area, observed_blocker, stated_impact, requested_change, evidence_quote, confidence and needs_follow_up. Missing facts must be “unknown.” Prohibit guesses about budget, company size, churn or market size. Check that every extracted field is supported by the quoted evidence.
Cluster problems, not keywords
“Import failed,” “Excel stalls” and “uploaded data disappears” may or may not share a task, condition and consequence. Ask AI for candidate clusters, shared evidence, distinct conditions and reasons not to merge. Confirm them with product or support staff and assign a stable cluster ID for future records.
Keep evidence and cost separate in prioritisation
Show four explicit factors: independent customer/channel coverage, task impact, evidence quality and cost to address. High/medium/low is often clearer than a mysterious combined score. A frequent complaint may be a documentation issue; a single well-evidenced blocker for a paying customer may deserve urgent attention.
Give every cluster a concrete next action
Actions include reproduce and fix, improve documentation, ask a follow-up, prototype, clarify packaging or defer with a reason. “To optimise” is not an action. Add an owner and date. After a change, ask original reporters whether the exact task can now be completed; do not merely ask whether they are satisfied.
Audit the ledger every two weeks: sample AI extractions, check over-merged clusters, confirm ownership and examine “unknown” fields. A high unknown rate often means the feedback form fails to ask for task and blocker context. With original evidence, traceable clusters and follow-up, AI becomes a useful organiser rather than an unaccountable product strategist.